• New functions added for determining and visualizing the optimal cutpoint of continuous variables for survival analyses:
  • surv_cutpoint(): Determine the optimal cutpoint for each variable using 'maxstat'. Methods defined for surv_cutpoint object are summary(), print() and plot().
  • surv_categorize(): Divide each variable values based on the cutpoint returned by surv_cutpoint()
• 0. Load some data

data(myeloma)
head(myeloma)

         molecular_group chr1q21_status treatment event  time   CCND1 CRIM1 DEPDC1    IRF4   TP53   WHSC1
GSM50986      Cyclin D-1       3 copies       TT2     0 69.24  9908.4 420.9  523.5 16156.5   10.0   261.9
GSM50988      Cyclin D-2       2 copies       TT2     0 66.43 16698.8  52.0   21.1 16946.2 1056.9   363.8
GSM50989           MMSET       2 copies       TT2     0 66.50   294.5 617.9  192.9  8903.9 1762.8 10042.9
GSM50990           MMSET       3 copies       TT2     1 42.67   241.9  11.9  184.7 11894.7  946.8  4931.0
GSM50991             MAF                  TT2     0 65.00   472.6  38.8  212.0  7563.1  361.4   165.0
GSM50992    Hyperdiploid       2 copies       TT2     0 65.20   664.1  16.9  341.6 16023.4 2096.3   569.2

• 1. Determine the optimal cutpoint of variables

# 1. Determine the optimal cutpoint of variables
res.cut <- surv_cutpoint(myeloma, time = "time", event = "event",
   variables = c("DEPDC1", "WHSC1", "CRIM1"))

summary(res.cut)

       cutpoint statistic
DEPDC1    279.8  4.275452
WHSC1    3205.6  3.361330
CRIM1      82.3  1.968317

• 2. Plot cutpoint for DEPDC1

# 2. Plot cutpoint for DEPDC1
# palette = "npg" (nature publishing group), see ?ggpubr::ggpar
plot(res.cut, "DEPDC1", palette = "npg")

• 3. Categorize variables

# 3. Categorize variables
res.cat <- surv_categorize(res.cut)
head(res.cat)

          time event DEPDC1 WHSC1 CRIM1
GSM50986 69.24     0   high   low  high
GSM50988 66.43     0    low   low   low
GSM50989 66.50     0    low  high  high
GSM50990 42.67     1    low  high   low
GSM50991 65.00     0    low   low   low
GSM50992 65.20     0   high   low   low

• 4. Fit survival curves and visualize

# 3. Fit survival curves and visualize
library("survival")
fit <- survfit(Surv(time, event) ~DEPDC1, data = res.cat)
ggsurvplot(fit, risk.table = TRUE, conf.int = TRUE)

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Well formated issue! I have to assume that during my cooperation with biotechnologist I have met the idea of examining expression of one gene in HIGH and LOW groups :) That's so true. I didn't know about maxstat method, so I always used median, as a natural approach.

The plot of maxstat looks very simple, but is very useful. Maybe we could somehow extend ggsurvplot to provide cutoffs for user while he/she only specifies names of the dependent continuous variables?

PS. I remember about vignette I was about to post here :P but just got invited on Bioc2016 and I am planning my journey and VISa.

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• This is extra idea to arm survminer with another tools!

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no emrgency for the vignette:-)!

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Wonderful and very helpful :)! good job

2016-10-22 20:20 GMT+02:00 Alboukadel KASSAMBARA [email protected]:

New functions added for determining and visualizing the optimal
cutpoint of continuous variables for survival analyses:

• surv_cutpoint(): Determine the optimal cutpoint for each variable
  using 'maxstat'. Methods defined for surv_cutpoint object are summary(),
  print() and plot().

  • surv_categorize(): Divide each variable values based on the
    cutpoint returned by surv_cutpoint()

  0. Load some data

data(myeloma)
head(myeloma)

     molecular_group chr1q21_status treatment event  time   CCND1 CRIM1 DEPDC1    IRF4   TP53   WHSC1

GSM50986 Cyclin D-1 3 copies TT2 0 69.24 9908.4 420.9 523.5 16156.5 10.0 261.9
GSM50988 Cyclin D-2 2 copies TT2 0 66.43 16698.8 52.0 21.1 16946.2 1056.9 363.8
GSM50989 MMSET 2 copies TT2 0 66.50 294.5 617.9 192.9 8903.9 1762.8 10042.9
GSM50990 MMSET 3 copies TT2 1 42.67 241.9 11.9 184.7 11894.7 946.8 4931.0
GSM50991 MAF TT2 0 65.00 472.6 38.8 212.0 7563.1 361.4 165.0
GSM50992 Hyperdiploid 2 copies TT2 0 65.20 664.1 16.9 341.6 16023.4 2096.3 569.2

• 1. Determine the optimal cutpoint of variables

1. Determine the optimal cutpoint of variablesres.cut <- surv_cutpoint(myeloma, time = "time", event = "event",

variables = c("DEPDC1", "WHSC1", "CRIM1"))

summary(res.cut)

   cutpoint statistic

DEPDC1 279.8 4.275452
WHSC1 3205.6 3.361330
CRIM1 82.3 1.968317

• * 2. Plot cutpoint for DEPDC1*

2. Plot cutpoint for DEPDC1# palette = "npg" (nature publishing group), see ?ggpubr::ggpar

plot(res.cut, "DEPDC1", palette = "npg")

[image: rplot16]
https://cloud.githubusercontent.com/assets/3313355/19621578/f63b9938-9894-11e6-930c-1f0efca81919.png

• 3. Categorize variables

3. Categorize variablesres.cat <- surv_categorize(res.cut)

head(res.cat)

      time event DEPDC1 WHSC1 CRIM1

GSM50986 69.24 0 high low high
GSM50988 66.43 0 low low low
GSM50989 66.50 0 low high high
GSM50990 42.67 1 low high low
GSM50991 65.00 0 low low low
GSM50992 65.20 0 high low low

• 4. Fit survival curves and visualize

3. Fit survival curves and visualize

library("survival")fit <- survfit(Surv(time, event) ~DEPDC1, data = res.cat)
ggsurvplot(fit, risk.table = TRUE, conf.int = TRUE)

[image: rplot15]
https://cloud.githubusercontent.com/assets/3313355/19621540/59b447f4-9894-11e6-8b5b-6a5add1769a7.png

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Thanks:-)!

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@kassambara Great work! Just wondering if this applies to the case when I try to find multiple cutpoints in one group, ie. 2 points to break into low/medium/high?

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